Molecular docking via quantum approximate optimization algorithm

TL;DR

This paper introduces a quantum algorithm called DC-QAOA for molecular docking, showing it outperforms classical methods in accuracy and hardware compatibility, thus advancing quantum-assisted drug discovery.

Contribution

The paper presents a novel quantum algorithm, DC-QAOA, tailored for molecular docking, demonstrating improved accuracy and suitability for noisy quantum hardware.

Findings

DC-QAOA provides more accurate docking results for complex biological systems.

Quantum algorithms like QAOA are more hardware-compatible in noisy intermediate-scale quantum devices.

The method shows promise for practical quantum-assisted drug discovery applications.

Abstract

Molecular docking plays a pivotal role in drug discovery and precision medicine, enabling us to understand protein functions and advance novel therapeutics. Here, we introduce a potential alternative solution to this problem, the digitized-counterdiabatic quantum approximate optimization algorithm (DC-QAOA), which utilizes counterdiabatic driving and QAOA on a quantum computer. Our method was applied to analyze diverse biological systems, including the SARS-CoV-2 Mpro complex with PM-2-020B, the DPP-4 complex with piperidine fused imidazopyridine 34, and the HIV-1 gp120 complex with JP-III-048. The DC-QAOA exhibits superior performance, providing more accurate and biologically relevant docking results, especially for larger molecular docking problems. Moreover, QAOA-based algorithms demonstrate enhanced hardware compatibility in the noisy intermediate-scale quantum era, indicating their potential for efficient implementation under practical docking scenarios. Our findings underscore quantum computing's potential in drug discovery and offer valuable insights for optimizing protein-ligand docking processes.